The 50 most-cited articles in Applied Sciences, 2017

The aim of this study was to prepare a novel targeting drug delivery system for 2-Methoxyestradiol (2ME) in order to improve the clinical application of this antitumor drug. It is based in nanoparticles (NPs) of titanium dioxide (TiO2) coated with polyethylene glycol (PEG) and loaded with 2ME. A complete IR and Raman characterization have been made to confirm the formation of TiO2–PEG–2ME composite. Vibrational modes have been assigned for TiO2, PEG, and 2ME and functionalized TiO2–PEG and TiO2–PEG–2ME. The observed variation in peak position of FTIR and Raman of each for these composites has been elucidated in terms of intermolecular interactions between PEG–2ME and TiO2, obtaining step-by-step the modification processes that were attributed to the conjugation of PEG and 2ME to TiO2 NPs. Modifying TiO2 NPs with PEG loaded with the 2ME drug revealed that the titanium dioxide nanocarrier possesses an effective adsorption capability, and we discuss their potential application as a system of drug delivery.

The modern power system is progressing from a synchronous machine-based system towards an inverter-dominated system, with large-scale penetration of renewable energy sources (RESs) like wind and photovoltaics. RES units today represent a major share of the generation, and the traditional approach of integrating them as grid following units can lead to frequency instability. Many researchers have pointed towards using inverters with virtual inertia control algorithms so that they appear as synchronous generators to the grid, maintaining and enhancing system stability. This paper presents a literature review of the current state-of-the-art of virtual inertia implementation techniques, and explores potential research directions and challenges. The major virtual inertia topologies are compared and classified. Through literature review and simulations of some selected topologies it has been shown that similar inertial response can be achieved by relating the parameters of these topologies through time constants and inertia constants, although the exact frequency dynamics may vary…

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to…

Energy storage systems (ESS) provide a means for improving the efficiency of electrical systems when there are imbalances between supply and demand. Additionally, they are a key element for improving the stability and quality of electrical networks. They add flexibility into the electrical system by mitigating the supply intermittency, recently made worse by an increased penetration of renewable generation. One energy storage technology now arousing great interest is the flywheel energy storage systems (FESS), since this technology can offer many advantages as an energy storage solution over the alternatives. Flywheels have attributes of a high cycle life, long operational life, high round-trip efficiency, high power density, low environmental impact, and can store megajoule (MJ) levels of energy with no upper limit when configured in banks. This paper presents a critical review of FESS in regards to its main components and applications, an approach not captured in earlier reviews. Additionally, earlier reviews do not include the most recent literature in this fast-moving field. A…

The directed energy deposition (DED) process can be employed to build net shape components or prototypes starting from powder or wires, through a layer-by-layer process. This process provides an opportunity to fabricate complex shaped and functionally graded parts that can be utilized in different engineering applications. DED uses a laser as a focused heat source to melt the in-situ delivered powder or wire-shaped raw materials. In the past years extensive studies on DED have shown that this process has great potential in order to be used for (i) rapid prototyping of metallic parts, (ii) fabrication of complex and customized parts, (iii) repairing/cladding valuable components which cannot be repaired by other traditional techniques. However, the industrial adoption of this process is still challenging owing to the lack of knowledge on the mechanical performances of the constructed components and also on the trustworthiness/durability of engineering parts produced by DED. This manuscript provides an overview of the additive manufacturing (AM) of titanium alloys and focuses in…

Synthetic Aperture RADAR Interferometry (InSAR) provides a unique tool for the quantitative measurement of the Earth’s surface deformations induced by a variety of natural (such as volcanic eruptions, landslides and earthquakes) and anthropogenic (e.g., ground-water extraction in highly-urbanized areas, deterioration of buildings and public facilities) processes. In this framework, use of InSAR technology makes it possible the long-term monitoring of surface deformations and the analysis of relevant geodynamic phenomena. This review paper provides readers with a general overview of the InSAR principles and the recent development of the advanced multi-track InSAR combination methodologies, which allow to discriminate the 3-D components of deformation processes and to follow their temporal evolution. The increasing availability of SAR data collected by complementary illumination angles and from different RADAR instruments, which operate in various bands of the microwave spectrum (X-, L- and C-band), makes the use of multi-track/multi-satellite InSAR techniques very promising for the…

The SwissFEL X-ray Free Electron Laser (XFEL) facility started construction at the Paul Scherrer Institute (Villigen, Switzerland) in 2013 and will be ready to accept its first users in 2018 on the Aramis hard X-ray branch. In the following sections we will summarize the various aspects of the project, including the design of the soft and hard X-ray branches of the accelerator, the results of SwissFEL performance simulations, details of the photon beamlines and experimental stations, and our first commissioning results.

Polylactic Acid (PLA) is an organic polymer commonly used in fused deposition (FDM) printing and biomedical scaffolding that is biocompatible and immunologically inert. However, variations in source material quality and chemistry make it necessary to characterize the filament and determine potential changes in chemistry occurring as a result of the FDM process. We used several spectroscopic techniques, including laser confocal microscopy, Fourier-Transform Infrared (FTIR) spectroscopy and photoacousitc FTIR spectroscopy, Raman spectroscopy, and X-ray photoelectron Spectroscopy (XPS) in order to characterize both the bulk and surface chemistry of the source material and printed samples. Scanning Electron Microscopy (SEM) and Differential Scanning Calorimetry (DSC) were used to characterize morphology, crystallinity, and the glass transition temperature following printing. Analysis revealed calcium carbonate-based additives which were reacted with organic ligands and potentially trace metal impurities, both before and following printing. These additives became concentrated in voids in…

European XFEL is a free-electron laser (FEL) user facility providing soft and hard X-ray FEL radiation to initially six scientific instruments. Starting user operation in fall 2017 European XFEL will provide new research opportunities to users from science domains as diverse as physics, chemistry, geo- and planetary sciences, materials sciences or biology. The unique feature of European XFEL is the provision of high average brilliance in the soft and hard X-ray regime, combined with the pulse properties of FEL radiation of extreme peak intensities, femtosecond pulse duration and high degree of coherence. The high average brilliance is achieved through acceleration of up to 27,000 electron bunches per second by the super-conducting electron accelerator. Enabling the usage of this high average brilliance in user experiments is one of the major instrumentation drivers for European XFEL. The radiation generated by three FEL sources is distributed via long beam transport systems to the experiment hall where the scientific instruments are located side-by-side. The X-ray beam transport…

Additive Manufacturing (AM) methods are generally used to produce an early sample or near net-shape elements based on three-dimensional geometrical modules. To date, publications on AM of metal implants have mainly focused on knee and hip replacements or bone scaffolds for tissue engineering. The direct fabrication of metallic implants can be achieved by methods, such as Selective Laser Melting (SLM) or Electron Beam Melting (EBM). This work compares the SLM and EBM methods used in the fabrication of titanium bone implants by analyzing the microstructure, mechanical properties and cytotoxicity. The SLM process was conducted in an environmental chamber using 0.4–0.6 vol % of oxygen to enhance the mechanical properties of a Ti-6Al-4V alloy. SLM processed material had high anisotropy of mechanical properties and superior UTS (1246–1421 MPa) when compared to the EBM (972–976 MPa) and the wrought material (933–942 MPa). The microstructure and phase composition depended on the used fabrication method. The AM methods caused the formation of long epitaxial grains of the prior β phase. The…

Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants’ emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results…

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal…

The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model) process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of…

Distributed optical fibre sensing, employing either Rayleigh, Raman, or Brillouin scattering, is the only physical-contact sensor technology capable of accurately estimating physical fields with spatial continuity along the fibre. This unique feature and the other features of standard optical fibre sensors (e.g., minimal invasiveness and lightweight, remote powering/interrogating capabilities) have for many years promoted the technology to be a promising candidate for geo-hydrological monitoring. Relentless research efforts are being undertaken to bring the technology to complete maturity through laboratory, physical models, and in-situ tests. The application of distributed optical fibre sensors to geo-hydrological monitoring is here reviewed and discussed, along with basic principles and main acquisition techniques. Among the many existing geo-hydrological processes, the emphasis is placed on those related to soil levees, slopes/landslide, and ground subsidence that constitute a significant percentage of current geohazards.

Equivalent circuit models are a hot research topic in the field of lithium-ion batteries for electric vehicles, and scholars have proposed a variety of equivalent circuit models, from simple to complex. On one hand, a simple model cannot simulate the dynamic characteristics of batteries; on the other hand, it is difficult to apply a complex model to a real-time system. At present, there are few systematic comparative studies on equivalent circuit models of lithium-ion batteries. The representative first-order resistor-capacitor (RC) model and second-order RC model commonly used in the literature are studied comparatively in this paper. Firstly, the parameters of the two models are identified experimentally; secondly, the simulation model is built in Matlab/Simulink environment, and finally the output precision of these two models is verified by the actual data. The results show that in the constant current condition, the maximum error of the first-order RC model is 1.65% and the maximum error for the second-order RC model is 1.22%. In urban dynamometer driving schedule (UDDS)…

Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then…

Traditionally, damage identification techniques in bridges have focused on monitoring changes to modal-based Damage Sensitive Features (DSFs) due to their direct relationship with structural stiffness and their spatial information content. However, their progression to real-world applications has not been without its challenges and shortcomings, mainly stemming from: (1) environmental and operational variations; (2) inefficient utilization of machine learning algorithms for damage detection; and (3) a general over-reliance on modal-based DSFs alone. The present paper provides an in-depth review of the development of modal-based DSFs and a synopsis of the challenges they face. The paper then sets out to addresses the highlighted challenges in terms of published advancements and alternatives from recent literature.

The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of…

In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to…

The introduction of mobile devices has changed our daily lives. They enable users to obtain information even in a nomadic environment and provide information without limitations. A decade after the introduction of this technology, we are now facing the next innovation that will change our daily lives. With the introduction of the Internet of Things (IoT), our communication ability will not be restricted to only mobile devices. Rather, it will expand to all things with which we coexist. Many studies have discussed IoT-related services and platforms. However, there are only limited discussions about the IoT network. In this paper, we will thoroughly analyze the technical details about the IoT network. Based on our survey of papers, we will provide insight about the future IoT network and the crucial components that will enable it.

In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

In order to build a better understanding of the relationship between depositing mode and porosity, microstructure, and properties in wire + arc additive manufacturing (WAAM) 2319-Al components, several Al-6.3%Cu deposits were produced by WAAM technique with cold metal transfer (CMT) variants, pulsed CMT (CMT-P) and advanced CMT (CMT-ADV). Thin walls and blocks were selected as the depositing paths to make WAAM samples. Porosity, microstructure and micro hardness of these WAAM samples were investigated. Compared with CMT-P and thin wall mode, CMT-ADV and block process can effectively reduce the pores in WAAM aluminum alloy. The microstructure varied with different depositing paths and CMT variants. The micro hardness value of thin wall samples was around 75 HV from the bottom to the middle, and gradually decreased toward the top. Meanwhile, the micro hardness value ranged around 72–77 HV, and varied periodically in block samples. The variation in micro hardness is consistent with standard microstructure characteristics.

The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural…

This review aims at introducing an engineering field of lubrication to researchers who are not familiar with tribology, thereby emphasizing the importance of lubricant chemistry in applied science. It provides initial guidance regarding additive chemistry in lubrication systems for researchers with different backgrounds. The readers will be introduced to molecular sciences underlying lubrication engineering. Currently, lubricant chemistry, especially “additive technology”, looks like a very complicated field. It seems that scientific information is not always shared by researchers. The cause of this is that lubrication engineering is based on empirical methods and focuses on market requirements. In this regard, engineering knowhow is held by individuals and is not being disclosed to scientific communities. Under these circumstances, a bird’s-eye view of lubricant chemistry in scientific words is necessary. The novelty of this review is to concisely explain the whole picture of additive technology in chemical terms. The roles and functions of additives as the leading actors in…

Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions…

Using phase change materials (PCMs) for thermal energy storage has always been a hot topic within the research community due to their excellent performance on energy conservation such as energy efficiency in buildings, solar domestic hot water systems, textile industry, biomedical and food agroindustry. Several literatures have reported phase change materials concerning various aspects. Among these materials, salt hydrates are worthy of exploring due to their high-energy storage density, rational price, multiple sources and relatively good thermal conductivity. This paper reviews the present state of salt hydrates PCMs targeting classification, properties, defects, possible solutions as well as their idiographic features which are suitable for applications. In addition, new trends of future research are also indicated.

Polarization sensitive optical coherence tomography (PS-OCT) is an imaging technique based on light scattering. PS-OCT performs rapid two- and three-dimensional imaging of transparent and translucent samples with micrometer scale resolution. PS-OCT provides image contrast based on the polarization state of backscattered light and has been applied in many biomedical fields as well as in non-medical fields. Thereby, the polarimetric approach enabled imaging with enhanced contrast compared to standard OCT and the quantitative assessment of sample polarization properties. In this article, the basic methodological principles, the state of the art of PS-OCT technologies, and important applications of the technique are reviewed in a concise yet comprehensive way.

Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage…

The magnetohydrodynamic thin film nanofluid sprayed on a stretching cylinder with heat transfer is explored. The spray rate is a function of film size. Constant reference temperature is used for the motion past an expanding cylinder. The sundry behavior of the magnetic nano liquid thin film is carefully noticed which results in to bring changes in the flow pattern and heat transfer. Water-based nanofluids like Al 2 O 3 -H 2 O and CuO-H 2 O are investigated under the consideration of thin film. The basic constitutive equations for the motion and transfer of heat of the nanofluid with the boundary conditions have been converted to nonlinear coupled differential equations with physical conditions by employing appropriate similarity transformations. The modeled equations have been computed by using HAM (Homotopy Analysis Method) and lead to detailed expressions for the velocity profile and temperature distribution. The pressure distribution and spray rate are also calculated. The comparison of HAM solution predicts the close agreement with the numerical method solution. The residual…

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility to support prevention, diagnostic work-up, therapeutic decisions, and individual disease monitoring with a continuous assessment of gait and mobility. This overview article provides the technological as well as medical aspects of smart shoes within this rising area of digital health applications, and is designed especially for the novel reader in this specific field. It also stresses the need for closer…

Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users’ recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and…

Halloysite nanotubes (HNTs) are natural occurring mineral clay nanotubes that have excellent application potential in different fields. However, HNTs are heterogeneous in size, surface charge and formation of surfacial hydrogen bond, which lead to weak affinity and aggregation at a certain extent. It is very important to modify the HNTs’ surface to expand its applications. In this review, the structural characteristics, performance and the related applications of surface-modified HNTs are reviewed. We focus on the surface-modified variation of HNTs, the effects of surface modification on the materials and related applications in various regions. In addition, future prospects and the meaning of surface modification were also discussed in HNTs studies. This review provides a reference for the application of HNTs modifications in the field of new nanomaterials.

There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field…

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied…

The construction of Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL), a 0.1-nm hard X-ray free-electron laser (FEL) facility based on a 10-GeV S-band linear accelerator (LINAC), is achieved in Pohang, Korea by the end of 2016. The construction of the 1.11 km-long building was completed by the end of 2014, and the installation of the 10-GeV LINAC and undulators started in January 2015. The installation of the 10-GeV LINAC, together with the undulators and beamlines, was completed by the end of 2015. The commissioning began in April 2016, and the first lasing of the hard X-ray FEL line was achieved on 14 June 2016. The progress of the PAL-XFEL construction and its commission are reported here.

As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time.[...]

Composites of hydroxyapatite (HAp) are widely employed in biomedical applications due to their biocompatibility, bioactivity and osteoconductivity properties. In fact, the development of industrially scalable hybrids at low cost and high efficiency has a great impact, for example, on bone tissue engineering applications and even as drug delivery systems. New nanocomposites constituted by HAp nanoparticles and synthetic or natural polymers with biodegradable and biocompatible characteristics have constantly been developed and extensive works have been published concerning their applications. The present review is mainly focused on both the capability of HAp nanoparticles to encapsulate diverse compounds as well as the preparation methods of scaffolds incorporating HAp. Attention has also been paid to the recent developments on antimicrobial scaffolds, bioactive membranes, magnetic scaffolds, in vivo imaging systems, hydrogels and coatings that made use of HAp nanoparticles.

New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling the acquisition of both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. The paper is, however, focused on the three innovative hyperspectral imaging-based techniques or sensing platforms, i.e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, which have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each…

In this article, a systematic overview of various recording and reproduction techniques for spatial audio is presented. While binaural recording and rendering is designed to resemble the human two-ear auditory system and reproduce sounds specifically for a listener’s two ears, soundfield recording and reproduction using a large number of microphones and loudspeakers replicate an acoustic scene within a region. These two fundamentally different types of techniques are discussed in the paper. A recent popular area, multi-zone reproduction, is also briefly reviewed in the paper. The paper is concluded with a discussion of the current state of the field and open problems.

The main aim of the present paper is to solve numerically the free vibration problem of sandwich shell structures with variable thickness and made of Functionally Graded Materials (FGMs). Several Higher-order Shear Deformation Theories (HSDTs), defined by a unified formulation, are employed in the study. The FGM structures are characterized by variable mechanical properties due to the through-the-thickness variation of the volume fraction distribution of the two constituents and the arbitrary thickness profile. A four-parameter power law expression is introduced to describe the FGMs, whereas general relations are used to define the thickness variation, which can affect both the principal coordinates of the shell reference domain. A local scheme of the Generalized Differential Quadrature (GDQ) method is employed as numerical tool. The natural frequencies are obtained varying the exponent of the volume fraction distributions using higher-order theories based on a unified formulation. The structural models considered are two-dimensional and require less degrees of freedom when compared…

In this article, we present an overview of aperture and apertureless type scanning near-field optical microscopy (SNOM) techniques that have been developed, with a focus on three-dimensional (3D) SNOM methods. 3D SNOM has been undertaken to image the local distribution (within ~100 nm of the surface) of the electromagnetic radiation scattered by random and deterministic arrays of metal nanostructures or photonic crystal waveguides. Individual metal nanoparticles and metal nanoparticle arrays exhibit unique effects under light illumination, including plasmon resonance and waveguiding properties, which can be directly investigated using 3D-SNOM. In the second part of this article, we will review a few applications in which 3D-SNOM has proven to be useful for designing and understanding specific nano-optoelectronic structures. Examples include the analysis of the nano-optical response phonetic crystal waveguides, aperture antennae and metal nanoparticle arrays, as well as the design of plasmonic solar cells incorporating random arrays of copper nanoparticles as an optical absorption…

The aim of this paper was to assess groundwater quality in a landfill and waste management site, with special regard to levels of organic pollution indicators: chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total organic carbon (TOC). Analysis of specific indicators was conducted for piezometers located in the area of the Radiowo landfill, the composting plant and the facilities adjacent to the landfill. The article discusses the temporal and spatial changes of selected organic pollution indicators. Based on the results of groundwater monitoring, several maps of COD, BOD and TOC areal distribution were provided. Statistical distribution of monitoring data was presented using box-and-whisker plots. Pearson’s correlation coefficients between selected pollution indicators were measured with a significance level set at p < 0.01 and p < 0.05. The strongest correlation was observed between BOD and COD. The maximum BOD/COD ratio was observed at the level of 1.561 before the closure of the vertical barrier, whereas, at present, average values of this ratio are below…

Dementia is the most prevalent degenerative disease in seniors in which progression can be prevented or delayed by early diagnosis. In this study, we proposed a two-layer model inspired by the method used in dementia support centers for the early diagnosis of dementia and using machine learning techniques. Data were collected from patients who received dementia screening from 2008 to 2013 at the Gangbuk-Gu center for dementia in the Republic of Korea. The data consisted of the patient’s gender, age, education, the Mini-Mental State Examination in the Korean version of the CERAD Assessment Packet (MMSE-KC) for dementia screening test, and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K) for the dementia precise test. In the proposed model, MMSE-KC data are initially classified into normal and abnormal. In the second stage, CERAD-K data are used to classify dementia and mild cognitive impairment. The performance of each algorithm is compared with that of Naive Bayes, Bayes Network, Begging, Logistic Regression, Random Forest, Support…

Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This paper proposes the Internet of Things (IoT)-based information system for indoor and outdoor use. Since the conducted survey of related works indicated a lack of methodological approaches to the design process, therefore a Design Methodology (DM), which approaches the design target from the perspective of the stakeholders, contracting authorities and potential users, is introduced. The implemented solution applies the three-axial accelerometer and magnetometer, Pedestrian Dead Reckoning (PDR), thresholding and the decision trees algorithm. Such an architecture enables the localization of a monitored person within four room-zones with accuracy; furthermore, it identifies falls and the activities of lying, standing, sitting and walking. Based on the identified activities, the system classifies current…

Additive manufacturing using tungsten, a brittle material, is difficult because of its high melting point, thermal conductivity, and oxidation tendency. In this study, pure tungsten parts with densities of up to 18.53 g/cm3 (i.e., 96.0% of the theoretical density) were fabricated by selective laser melting. In order to minimize balling effects, the raw polyhedral tungsten powders underwent a spheroidization process before laser consolidation. Compared with polyhedral powders, the spherical powders showed increased laser absorptivity and packing density, which helped in the formation of a continuous molten track and promoted densification.

In this paper, an experimental study was conducted to determine the effect of different cutting parameters such as cutting speed, feed rate, and depth of cut on cutting force, surface roughness, and tool wear in the milling of Ti-6242S alloy using the cemented carbide (WC) end mills with a 10 mm diameter. Data obtained from experiments were defined both Artificial Neural Network (ANN) and Response Surface Methodology (RSM). ANN trained network using Levenberg-Marquardt (LM) and weights were trained. On the other hand, the mathematical models in RSM were created applying Box Behnken design. Values obtained from the ANN and the RSM was found to be very close to the data obtained from experimental studies. The lowest cutting force and surface roughness were obtained at high cutting speeds and low feed rate and depth of cut. The minimum tool wear was obtained at low cutting speed, feed rate, and depth of cut.

Lithium-ion batteries (LIBs) have drawn considerable attention due to their high energy density and good cycling stability. As a transition-metal oxide, MgCo2O4 (MCO) is a promising candidate for energy storage applications because of its low-cost and environmental characteristics. Here, MCO porous nanosheets have been successfully synthesized by a microwave-assisted liquid phase method followed by an annealing procedure. As a result, MCO annealed at 600 °C exhibited optimal rate and cycling performances for Lithium storage application. Specifically, when tested as anode materials for Lithium ion batteries, MCO porous nanosheets delivered a high specific capacity of 1173.8 mAh g−1 at 200 mA g−1, and the specific capacity reached 1130.1 mAh g−1 after 100 cycles at 200 mA g−1, achieving 96.3% for the retention rate. The excellent electrochemical performances are mainly attributed to the monolayer porous nanosheet, which provides short transport paths for Li ions and electrons. Results demonstrated that the MCO porous nanosheets are promising electrode materials for Lithium ion…

The rapid printing of 3D parts with desired electrical properties enables numerous applications. Fused deposition modeling (FDM) using conductive thermoplastic composites has been a valuable approach for such fabrication. The parts produced by FDM possess various controllable structural features, but the effects of the structural features on the electrical properties remain to be determined. This study investigated the effects of these features on the electrical resistivity and resistivity anisotropy of 3D-printed ABS/CB composites. The effects of the process parameters of FDM, including the layer thickness, raster width, and air gap, on the resistivity in both the vertical and horizontal directions for cubic samples were studied because the internal structure of the printed parts depended on those process parameters. The resistivities of printed parts in different parameter combinations were measured by an impedance analyzer and finite element models were created to investigate the relationship between the resistivity and the internal structure. The results indicated that the…

Graphene oxide (GO) has a unique layered structure with excellent gas and liquid blocking properties. It is widely used in many areas, such as gas sensors, carbon-based electronics, impermeable membranes, and polymeric composite materials. In order to evaluate whether GO (1% and 3% by weight of asphalt) can improve the aging resistance performance of the asphalt, 80/100 penetration grade asphalt (90 A) and styrene–butadiene–styrene modified asphalt (SBS MA) were used to prepare the GO modified asphalt by the melt blending method. The surface morphology of the GO was analyzed by scanning electron microscope (SEM). The UV aging test was conducted to simulate the aging during the service period. After UV aging test, the physical performances of GO-modified asphalts were tested, and the IC=O and IS=O increments were tested by Fourier transform infrared spectroscopy (FTIR) to evaluate the aging resistance performance of the GO modified asphalt. In addition, the rheological properties of GO modified asphalts were studied using a dynamic shear rheometer (DSR). The SEM analysis indicated…

Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis…